VideoAesthetics

Aesthetics in photography is how people usually characterize beauty in this form of art.

High-level judgments, i.e., users' perception, of design have been shown to be correlated to low-level features of the appearance. Prior works suggest that the aesthetic and affective responses aroused by the visual appearnace of a design influence users' perception and experience.

Feature extraction

1. HandCraft feature on image

[1] Wu, Z., Kim, T., Li, Q., & Ma, X. (2019). Understanding and Modeling User-Perceived Brand Personality from Mobile Application UIs.

[2] Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006, May). Studying aesthetics in photographic images using a computational approach. In European conference on computer vision (pp. 288-301). Springer, Berlin, Heidelberg.

2. DeepLearning feature on image

[1] Ren, J., Shen, X., Lin, Z., Mech, R., & Foran, D. J. (2017). Personalized image aesthetics. In Proceedings of the IEEE International Conference on Computer Vision (pp. 638-647).

[2] Malu, G., Bapi, R. S., & Indurkhya, B. (2017). Learning photography aesthetics with deep cnns. arXiv preprint arXiv:1707.03981.

Key frame extraction

[1] Sheena, C. V., & Narayanan, N. K. (2015). Key-frame extraction by analysis of histograms of video frames using statistical methods. Procedia Computer Science, 70, 36-40.

[2] De Avila, S. E. F., Lopes, A. P. B., da Luz Jr, A., & de Albuquerque Araújo, A. (2011). VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recognition Letters, 32(1), 56-68.

Feature aggregation

[1] Jégou, H., Douze, M., Schmid, C., & Pérez, P. (2010, June). Aggregating local descriptors into a compact image representation. In CVPR 2010-23rd IEEE Conference on Computer Vision & Pattern Recognition (pp. 3304-3311). IEEE Computer Society.